Abstract
Palm region of interest (ROI) extraction is an indispensable procedure in palmprint recognition. Prior works generally perform well on palm ROI extraction because of dedicated devices and well-controlled environment. To make hand placement less-constrained and improve usability, mobile palmprint recognition has attracted a wide attention in recent years. For mobile phone images captured in complex natural environment, palm ROI extraction is a challenging work due to varying illumination, complex background and contactless acquisition mode. In this paper, a mobile palmprint dataset (SPIC) is at first established with five smartphones, comprising 4000 images collected from 128 persons in two separate sessions. Furthermore, a novel pre-processing approach is proposed to achieve ROI extraction in mobile scenarios, which include colour component selection, learning-based fast hand segmentation and geometry-driven valley point location. Experimental results demonstrate that the proposed method can achieve high extraction accuracy and computational efficiency on PolyU1.0, HA-BJTU and SPIC palmprint databases.
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